The Rise of Neural Networks for Materials and Chemical Dynamics
Abstract
Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Utah State Univ., Logan, UT (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Utah State Univ., Logan, UT (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1825448
- Report Number(s):
- LA-UR-21-23254
Journal ID: ISSN 1948-7185
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Physical Chemistry Letters
- Additional Journal Information:
- Journal Volume: 12; Journal Issue: 26; Journal ID: ISSN 1948-7185
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Elements; Molecular mechanics; Energy; Neural networks; Molecules
Citation Formats
Kulichenko, Maksim, Smith, Justin Steven, Nebgen, Benjamin Tyler, Li, Ying Wai, Fedik, Nikita Sergeyevich, Boldyrev, Alexander I., Lubbers, Nicholas Edward, Barros, Kipton Marcos, and Tretiak, Sergei. The Rise of Neural Networks for Materials and Chemical Dynamics. United States: N. p., 2021.
Web. doi:10.1021/acs.jpclett.1c01357.
Kulichenko, Maksim, Smith, Justin Steven, Nebgen, Benjamin Tyler, Li, Ying Wai, Fedik, Nikita Sergeyevich, Boldyrev, Alexander I., Lubbers, Nicholas Edward, Barros, Kipton Marcos, & Tretiak, Sergei. The Rise of Neural Networks for Materials and Chemical Dynamics. United States. https://doi.org/10.1021/acs.jpclett.1c01357
Kulichenko, Maksim, Smith, Justin Steven, Nebgen, Benjamin Tyler, Li, Ying Wai, Fedik, Nikita Sergeyevich, Boldyrev, Alexander I., Lubbers, Nicholas Edward, Barros, Kipton Marcos, and Tretiak, Sergei. Thu .
"The Rise of Neural Networks for Materials and Chemical Dynamics". United States. https://doi.org/10.1021/acs.jpclett.1c01357. https://www.osti.gov/servlets/purl/1825448.
@article{osti_1825448,
title = {The Rise of Neural Networks for Materials and Chemical Dynamics},
author = {Kulichenko, Maksim and Smith, Justin Steven and Nebgen, Benjamin Tyler and Li, Ying Wai and Fedik, Nikita Sergeyevich and Boldyrev, Alexander I. and Lubbers, Nicholas Edward and Barros, Kipton Marcos and Tretiak, Sergei},
abstractNote = {Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.},
doi = {10.1021/acs.jpclett.1c01357},
journal = {Journal of Physical Chemistry Letters},
number = 26,
volume = 12,
place = {United States},
year = {Thu Jul 01 00:00:00 EDT 2021},
month = {Thu Jul 01 00:00:00 EDT 2021}
}
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